#!/usr/bin/env python
# coding=utf-8
#!/usr/bin/env python
# coding=utf-8
# Copyright The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
import sys
sys.path.append('.')
import argparse
import functools
import gc
import itertools
import json
import logging
import math
import os
import random
import shutil
from pathlib import Path
from typing import List, Union

import accelerate
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import torchvision.transforms.functional as TF
import transformers
import webdataset as wds
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from braceexpand import braceexpand
from huggingface_hub import create_repo
from packaging import version
from peft import LoraConfig, get_peft_model, get_peft_model_state_dict, set_peft_model_state_dict
from torch.utils.data import default_collate
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import AutoTokenizer, CLIPTextModel, PretrainedConfig
from webdataset.tariterators import (
    base_plus_ext,
    tar_file_expander,
    url_opener,
    valid_sample,
)

import diffusers
from diffusers import (
    AutoencoderKL,
    DDPMScheduler,
    LCMScheduler,
    StableDiffusionPipeline,
    UNet2DConditionModel,
)
from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version, is_wandb_available
from diffusers.utils.import_utils import is_xformers_available
from opensora.registry import DATASETS, MODELS, SCHEDULERS, build_module
from opensora.acceleration.parallel_states import (
    get_data_parallel_group,
    set_data_parallel_group,
    set_sequence_parallel_group,
)
from torch.distributions import LogisticNormal
# from opensora.datasets import prepare_dataloader, prepare_variable_dataloader
MAX_SEQ_LENGTH = 77
from opensora.models.text_encoder.t5 import text_preprocessing
from torch.utils.data import DataLoader, Dataset
if is_wandb_available():
    import wandb
from opensora.datasets import IMG_FPS, save_sample
import time

# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.18.0.dev0")

logger = get_logger(__name__)
num_of_gpu = torch.cuda.device_count()

def timestep_transform(
    t,
    model_kwargs,
    base_resolution=512 * 512,
    base_num_frames=1,
    scale=1.0,
    num_timesteps=1,
):
    t = t / num_timesteps
    resolution = model_kwargs["height"][0] * model_kwargs["width"][0]
    ratio_space = (resolution / base_resolution).sqrt()
    # NOTE: currently, we do not take fps into account
    # NOTE: temporal_reduction is hardcoded, this should be equal to the temporal reduction factor of the vae
    if model_kwargs["num_frames"][0] == 1:
        num_frames = torch.ones_like(model_kwargs["num_frames"])
    else:
        num_frames = model_kwargs["num_frames"][0] // 17 * 5
    ratio_time = (num_frames / base_num_frames).sqrt()

    ratio = ratio_space * ratio_time * scale
    new_t = ratio * t / (1 + (ratio - 1) * t)

    new_t = new_t * num_timesteps
    return new_t
def get_module_kohya_state_dict(module, prefix: str, dtype: torch.dtype, adapter_name: str = "default"):
    kohya_ss_state_dict = {}
    for peft_key, weight in get_peft_model_state_dict(module, adapter_name=adapter_name).items():
        kohya_key = peft_key.replace("base_model.model", prefix)
        kohya_key = kohya_key.replace("lora_A", "lora_down")
        kohya_key = kohya_key.replace("lora_B", "lora_up")
        kohya_key = kohya_key.replace(".", "_", kohya_key.count(".") - 2)
        kohya_ss_state_dict[kohya_key] = weight.to(dtype)

        # Set alpha parameter
        if "lora_down" in kohya_key:
            alpha_key = f'{kohya_key.split(".")[0]}.alpha'
            kohya_ss_state_dict[alpha_key] = torch.tensor(module.peft_config[adapter_name].lora_alpha).to(dtype)

    return kohya_ss_state_dict


def log_validation(vae, dit, args, accelerator, weight_dtype, step, text_encoder, mode):
    logger.info("Running validation... ")
    sample_steps = [5, 10, 20]
    scheduler_table = []
    for sample_step in sample_steps:
        # scheduler_config = dict(
        #     type="iddpm",
        #     num_sampling_steps=sample_step,
        #     cfg_scale=4.0,
        #     cfg_channel=3,  # or None
        # )
        scheduler_config = dict(
            type="rflow",
            use_timestep_transform=True,
            num_sampling_steps=sample_step,
            cfg_scale=4.0,
        )
        scheduler_table.append(build_module(scheduler_config, SCHEDULERS))

    model = accelerator.unwrap_model(dit)
    latent_size = vae.get_latent_size((17, args.resolution, args.resolution))
    text_encoder.y_embedder = model.y_embedder 

    if args.seed is None:
        generator = None
    else:
        generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)

    # validation_prompts = [
    #     "portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography",
    #     "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k",
    #     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    #     "A photo of beautiful mountain with realistic sunset and blue lake, highly detailed, masterpiece",
    # ]
    validation_prompts = [
    #    "several tall pine trees on the snow covered mountain peak",
       #"boundless sea fulls of surging waves and mixed with wind and rain",

    #    "sunny and sunny coast, waves wave after wave hit the beach",
        "a clear stream flows slowly in the quiet forest. aesthetic score : 6.5"
    ]


    # videos_logs = []
    model_args = {}
    model_args["height"] = torch.tensor([256], device=accelerator.device).repeat(1)
    model_args["width"]  = torch.tensor([256], device=accelerator.device).repeat(1)
    model_args["num_frames"] = torch.tensor([17], device=accelerator.device).repeat(1)
    model_args["fps"] = torch.tensor([24], device=accelerator.device).repeat(1)
    model_args["ar"] = torch.tensor([1.0], device=accelerator.device).repeat(1)
    for i, prompt in enumerate(validation_prompts):
        videos = []
        with torch.no_grad():
            with torch.autocast("cuda", dtype=weight_dtype):
                for j, sample_step in enumerate(sample_steps):
                    z = torch.randn(1, vae.out_channels, *latent_size, device=accelerator.device, dtype=weight_dtype)
                   
                    samples = scheduler_table[j].no_cfg_sample(
                        model,
                        text_encoder,
                        z=z,
                        prompts = [text_preprocessing(prompt)],
                        device=accelerator.device,
                        additional_args = model_args,
                        # num_of_inference_step = sample_step
                    )
                    vidoes = vae.decode(samples.to(weight_dtype))
                    if not os.path.exists(os.path.join(args.output_dir, f"sample_change/{mode}/{step}/{sample_step}_step")):
                        os.makedirs(os.path.join(args.output_dir, f"sample_change/{mode}/{step}/{sample_step}_step"))
                    save_sample(vidoes[0], save_path=os.path.join(args.output_dir, f"sample_change/{mode}/{step}/{sample_step}_step/{i}"))
        torch.cuda.empty_cache()
    del scheduler_table


# From LatentConsistencyModel.get_guidance_scale_embedding
def guidance_scale_embedding(w, embedding_dim=512, dtype=torch.float32):
    """
    See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298

    Args:
        timesteps (`torch.Tensor`):
            generate embedding vectors at these timesteps
        embedding_dim (`int`, *optional*, defaults to 512):
            dimension of the embeddings to generate
        dtype:
            data type of the generated embeddings

    Returns:
        `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
    """
    assert len(w.shape) == 1
    w = w * 1000.0

    half_dim = embedding_dim // 2
    emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
    emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
    emb = w.to(dtype)[:, None] * emb[None, :]
    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
    if embedding_dim % 2 == 1:  # zero pad
        emb = torch.nn.functional.pad(emb, (0, 1))
    assert emb.shape == (w.shape[0], embedding_dim)
    return emb


def append_dims(x, target_dims):
    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
    dims_to_append = target_dims - x.ndim
    if dims_to_append < 0:
        raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less")
    return x[(...,) + (None,) * dims_to_append]


# From LCMScheduler.get_scalings_for_boundary_condition_discrete
def scalings_for_boundary_conditions(timestep, sigma_data=0.5, timestep_scaling=10.0):
    timestep = timestep / timestep_scaling
    c_skip = sigma_data**2 / (timestep ** 2 + sigma_data**2)
    c_out = timestep / (timestep ** 2 + sigma_data**2) ** 0.5
    return c_skip, c_out


# Compare LCMScheduler.step, Step 4
def predicted_origin(model_output, timesteps, sample, prediction_type, noise_scheduler):
    B, C = model_output.shape[:2]
    model_output, model_var_values = torch.split(model_output, C // 2, dim=1)
    # min_log = extract_into_tensor(noise_scheduler.posterior_log_variance_clipped, timesteps, sample.shape)
    # max_log = extract_into_tensor(torch.log(noise_scheduler.betas), timesteps, sample.shape)
    # # The model_var_values is [-1, 1] for [min_var, max_var].
    # frac = (model_var_values + 1) / 2
    # model_log_variance = frac * max_log + (1 - frac) * min_log
    # model_variance = torch.exp(0.5 * model_log_variance)
    
    if prediction_type == "epsilon":
        sigmas = extract_into_tensor(noise_scheduler.sqrt_one_minus_alphas_cumprod, timesteps, sample.shape)
        alphas = extract_into_tensor(noise_scheduler.sqrt_alphas_cumprod, timesteps, sample.shape)
        # print(model_variance.mean(), sigmas.mean())
        # print(model_variance.std(), sigmas.std())
        # exit(0)

        pred_x_0 = (sample - sigmas * model_output) / alphas
    elif prediction_type == "v_prediction":
        dt = timesteps / 1000
        dt = dt.unsqueeze(1).unsqueeze(1).unsqueeze(1).unsqueeze(1)
        dt = dt.repeat(1, *model_output.shape[1:])
        pred_x_0 = sample -  dt * model_output
    else:
        raise ValueError(f"Prediction type {prediction_type} currently not supported.")

    return pred_x_0
def get_model_output(pred_output, timesteps, sample, alphas, sigmas):
    sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)
    alphas = extract_into_tensor(alphas, timesteps, sample.shape)
    model_output = (sample - pred_output * alphas) / sigmas
    return model_output


def extract_into_tensor(a, t, x_shape):
    b, *_ = t.shape
    out = a.gather(-1, t)
    return out.reshape(b, *((1,) * (len(x_shape) - 1)))


class DDIMSolver:
    def __init__(self, alpha_cumprods, timesteps=1000, ddim_timesteps=50):
        # DDIM sampling parameters
        step_ratio = timesteps // ddim_timesteps
        self.ddim_timesteps = (np.arange(1, ddim_timesteps + 1) * step_ratio).round().astype(np.int64) - 1
        self.ddim_alpha_cumprods = alpha_cumprods[self.ddim_timesteps]
        self.ddim_alpha_cumprods_prev = np.asarray(
            [alpha_cumprods[0]] + alpha_cumprods[self.ddim_timesteps[:-1]].tolist()
        )
        # convert to torch tensors
        self.ddim_timesteps = torch.from_numpy(self.ddim_timesteps).long()
        self.ddim_alpha_cumprods = torch.from_numpy(self.ddim_alpha_cumprods)
        self.ddim_alpha_cumprods_prev = torch.from_numpy(self.ddim_alpha_cumprods_prev)
        # print(self.ddim_alpha_cumprods, self.ddim_alpha_cumprods_prev)
        # print(self.ddim_timesteps, self.ddim_timesteps[:-1])
        # exit()

    def to(self, device):
        self.ddim_timesteps = self.ddim_timesteps.to(device)
        self.ddim_alpha_cumprods = self.ddim_alpha_cumprods.to(device)
        self.ddim_alpha_cumprods_prev = self.ddim_alpha_cumprods_prev.to(device)
        return self

    def ddim_step(self, pred_x0, pred_noise, timestep_index):
        alpha_cumprod_prev = extract_into_tensor(self.ddim_alpha_cumprods_prev, timestep_index, pred_x0.shape)
        dir_xt = (1.0 - alpha_cumprod_prev).sqrt() * pred_noise
        x_prev = alpha_cumprod_prev.sqrt() * pred_x0 + dir_xt
        return x_prev

class EulerSolver:
    def __init__(self, timesteps=1000, sample_timesteps=50, model_args = {}):
        # DDIM sampling parameters
        self.timesteps = timesteps
        step_ratio = timesteps // sample_timesteps
        sample_timesteps = (np.arange(1, sample_timesteps + 1) * step_ratio).round().astype(np.int64) - 1
        sample_timesteps_transformed = [timestep_transform(t, model_args, num_timesteps=timesteps) for t in sample_timesteps]
        self.sample_timesteps = np.array(sample_timesteps_transformed)


        
        # convert to torch tensors
        self.sample_timesteps = torch.from_numpy(self.sample_timesteps).long()
        # print(self.ddim_alpha_cumprods, self.ddim_alpha_cumprods_prev)
        # print(self.ddim_timesteps, self.ddim_timesteps[:-1])
        # exit()

    def to(self, device):
        self.sample_timesteps = self.sample_timesteps.to(device)
        return self

    def euler_step(self, pred_x0, pred_noise, timestep_index):
        # print(f"euler step:{self.sample_timesteps[timestep_index]}")
        timepoints = self.sample_timesteps[timestep_index] / self.timesteps
        # timepoints = 1 - timepoints  # [1,1/1000]
        timepoints = timepoints.unsqueeze(1).unsqueeze(1).unsqueeze(1).unsqueeze(1)
        timepoints = timepoints.repeat(1, *pred_x0.shape[1:])
        #return timepoints * pred_x0 + (1 - timepoints) * pred_noise
        return pred_x0 + timepoints * pred_noise
@torch.no_grad()
def update_ema(target_params, source_params, rate=0.99):
    """
    Update target parameters to be closer to those of source parameters using
    an exponential moving average.

    :param target_params: the target parameter sequence.
    :param source_params: the source parameter sequence.
    :param rate: the EMA rate (closer to 1 means slower).
    """
    for targ, src in zip(target_params, source_params):
        targ.detach().mul_(rate).add_(src, alpha=1 - rate)

def sample_t(x, loc = 0.0, scale = 1.0):
    distribution = LogisticNormal(torch.tensor([loc]), torch.tensor([scale]))
    t = distribution.sample((x.shape[0],))[:, 0].to(x.device) * 1000
    return t

def sample_s_from_t(t, stage, q=15):
    
    # decay = 1 / q ** (4 - stage)
    # ratio = 1 - decay
    r = t - (stage + 1) * q
    return torch.clamp(r, min=0)



def parse_args():
    parser = argparse.ArgumentParser(description="Simple example of a training script.")
    # ----------Model Checkpoint Loading Arguments----------
    parser.add_argument(
        "--pretrained_teacher_model",
        type=str,
        default=None,
        required=False,
        help="Path to pretrained LDM teacher model or model identifier from huggingface.co/models.",
    )
    parser.add_argument(
        "--pretrained_vae_model_name_or_path",
        type=str,
        default=None,
        help="Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.",
    )
    parser.add_argument(
        "--teacher_revision",
        type=str,
        default=None,
        required=False,
        help="Revision of pretrained LDM teacher model identifier from huggingface.co/models.",
    )
    parser.add_argument(
        "--revision",
        type=str,
        default=None,
        required=False,
        help="Revision of pretrained LDM model identifier from huggingface.co/models.",
    )
    # ----------Training Arguments----------
    # ----General Training Arguments----
    parser.add_argument(
        "--output_dir",
        type=str,
        default="/g0022010zlc/zyz/opensora_distill/checkpoints/all_webvid_mixed_precision_fp16_bs_4",
        help="The output directory where the model predictions and checkpoints will be written.",
    )
    parser.add_argument(
        "--cache_dir",
        type=str,
        default=None,
        help="The directory where the downloaded models and datasets will be stored.",
    )
    parser.add_argument("--seed", type=int, default=123, help="A seed for reproducible training.")
    # ----Logging----
    parser.add_argument(
        "--logging_dir",
        type=str,
        default="logs",
        help=(
            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
        ),
    )
    parser.add_argument(
        "--report_to",
        type=str,
        default="tensorboard",
        help=(
            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
        ),
    )
    # ----Checkpointing----
    parser.add_argument(
        "--checkpointing_steps",
        type=int,
        default=5000,
        help=(
            "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
            " training using `--resume_from_checkpoint`."
        ),
    )
    parser.add_argument(
        "--checkpoints_total_limit",
        type=int,
        default=None,
        help=("Max number of checkpoints to store."),
    )
    parser.add_argument(
        "--resume_from_checkpoint",
        type=str,
        default=None,
        help=(
            "Whether training should be resumed from a previous checkpoint. Use a path saved by"
            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
        ),
    )
    # ----Image Processing----
    parser.add_argument(
        "--train_shards_path_or_url",
        type=str,
        default=None,
        help=(
            "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
            " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
            " or to a folder containing files that 🤗 Datasets can understand."
        ),
    )
    parser.add_argument(
        "--resolution",
        type=int,
        default=256,
        help=(
            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
            " resolution"
        ),
    )
    parser.add_argument(
        "--center_crop",
        default=False,
        action="store_true",
        help=(
            "Whether to center crop the input images to the resolution. If not set, the images will be randomly"
            " cropped. The images will be resized to the resolution first before cropping."
        ),
    )
    parser.add_argument(
        "--random_flip",
        action="store_true",
        help="whether to randomly flip images horizontally",
    )
    # ----Dataloader----
    parser.add_argument(
        "--dataloader_num_workers",
        type=int,
        default=0,
        help=(
            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
        ),
    )
    # ----Batch Size and Training Steps----
    parser.add_argument(
        "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
    )
    parser.add_argument("--num_train_epochs", type=int, default=10)
    parser.add_argument(
        "--max_train_steps",
        type=int,
        default=25000,
        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
    )
    parser.add_argument(
        "--max_train_samples",
        type=int,
        default=None,
        help=(
            "For debugging purposes or quicker training, truncate the number of training examples to this "
            "value if set."
        ),
    )
    # ----Learning Rate----
    parser.add_argument(
        "--learning_rate",
        type=float,
        default=1e-6,
        help="Initial learning rate (after the potential warmup period) to use.",
    )
    parser.add_argument(
        "--scale_lr",
        action="store_true",
        default=False,
        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
    )
    parser.add_argument(
        "--lr_scheduler",
        type=str,
        default="constant",
        help=(
            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
            ' "constant", "constant_with_warmup"]'
        ),
    )
    parser.add_argument(
        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
    )
    parser.add_argument(
        "--gradient_accumulation_steps",
        type=int,
        default=1,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )
    # ----Optimizer (Adam)----
    parser.add_argument(
        "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
    )
    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
    parser.add_argument("--adam_epsilon", type=float, default=1e-15, help="Epsilon value for the Adam optimizer")
    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
    # ----Diffusion Training Arguments----
    parser.add_argument(
        "--proportion_empty_prompts",
        type=float,
        default=0,
        help="Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).",
    )
    # ----Latent Consistency Distillation (LCD) Specific Arguments----
    parser.add_argument(
        "--w_min",
        type=float,
        default=6.5,
        required=False,
        help=(
            "The minimum guidance scale value for guidance scale sampling. Note that we are using the Imagen CFG"
            " formulation rather than the LCM formulation, which means all guidance scales have 1 added to them as"
            " compared to the original paper."
        ),
    )
    parser.add_argument(
        "--w_max",
        type=float,
        default=7.5,
        required=False,
        help=(
            "The maximum guidance scale value for guidance scale sampling. Note that we are using the Imagen CFG"
            " formulation rather than the LCM formulation, which means all guidance scales have 1 added to them as"
            " compared to the original paper."
        ),
    )
    parser.add_argument(
        "--num_ddim_timesteps",
        type=int,
        default=50,
        help="The number of timesteps to use for DDIM sampling.",
    )
    parser.add_argument(
        "--loss_type",
        type=str,
        default="huber",
        choices=["l2", "huber"],
        help="The type of loss to use for the LCD loss.",
    )
    parser.add_argument(
        "--huber_c",
        type=float,
        default=0.01,
        help="The huber loss parameter. Only used if `--loss_type=huber`.",
    )
    parser.add_argument(
        "--lora_rank",
        type=int,
        default=64,
        help="The rank of the LoRA projection matrix.",
    )
    # ----Mixed Precision----
    parser.add_argument(
        "--mixed_precision",
        type=str,
        default="fp16",
        choices=["no", "fp16", "bf16"],
        help=(
            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
        ),
    )
    parser.add_argument(
        "--allow_tf32",
        action="store_true",
        help=(
            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
        ),
    )
    parser.add_argument(
        "--cast_teacher_unet",
        action="store_true",
        help="Whether to cast the teacher U-Net to the precision specified by `--mixed_precision`.",
    )
    parser.add_argument(
        "--freeze_embed",
        action="store_true",
        help="only train dit",
    )
    # ----Training Optimizations----
    parser.add_argument(
        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
    )
    parser.add_argument(
        "--gradient_checkpointing",
        action="store_true",
        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
    )
    # ----Distributed Training----
    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
    # ----------Validation Arguments----------
    parser.add_argument(
        "--validation_steps",
        type=int,
        default=500,
        help="Run validation every X steps.",
    )
    # ----------Huggingface Hub Arguments-----------
    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
    parser.add_argument(
        "--hub_model_id",
        type=str,
        default=None,
        help="The name of the repository to keep in sync with the local `output_dir`.",
    )
    # ----------Accelerate Arguments----------
    parser.add_argument(
        "--tracker_project_name",
        type=str,
        default="delta=30",
        help=(
            "The `project_name` argument passed to Accelerator.init_trackers for"
            " more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
        ),
    )

    args = parser.parse_args()
    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
    if env_local_rank != -1 and env_local_rank != args.local_rank:
        args.local_rank = env_local_rank

    if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:
        raise ValueError("`--proportion_empty_prompts` must be in the range [0, 1].")

    return args


# Adapted from pipelines.StableDiffusionPipeline.encode_prompt
def encode_prompt(prompt_batch, text_encoder, tokenizer, proportion_empty_prompts, is_train=True):
    captions = []
    for caption in prompt_batch:
        if random.random() < proportion_empty_prompts:
            captions.append("")
        elif isinstance(caption, str):
            captions.append(caption)
        elif isinstance(caption, (list, np.ndarray)):
            # take a random caption if there are multiple
            captions.append(random.choice(caption) if is_train else caption[0])

    with torch.no_grad():
        text_inputs = tokenizer(
            captions,
            padding="max_length",
            max_length=tokenizer.model_max_length,
            truncation=True,
            return_tensors="pt",
        )
        text_input_ids = text_inputs.input_ids
        prompt_embeds = text_encoder.encode(text_input_ids.to(text_encoder.device))["y"]

    return prompt_embeds


def main(args):
    
    logging_dir = Path(args.output_dir, args.logging_dir)

    accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)

    accelerator = Accelerator(
        gradient_accumulation_steps=args.gradient_accumulation_steps,
        mixed_precision=args.mixed_precision,
        #downcast_bf16=True,
        log_with=args.report_to,
        device_placement=True,
        project_config=accelerator_project_config,
        split_batches=True,  # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be devide by the number of processes assuming batches are multiplied by the number of processes
    )

    # Make one log on every process with the configuration for debugging.
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO,
    )
    logger.info(accelerator.state, main_process_only=False)
    if accelerator.is_local_main_process:
        transformers.utils.logging.set_verbosity_warning()
        diffusers.utils.logging.set_verbosity_info()
    else:
        transformers.utils.logging.set_verbosity_error()
        diffusers.utils.logging.set_verbosity_error()
    # .init(
    # # set the wandb project where this run will be logged
    #     project="shuffuling data training",

    #     # track hyperparameters and run metadata
    #     config={
    #         "learning_rate": 1e-6,
    #         "dataset": "opensoraplan + webbvid",
    #         "training_steps": args.max_train_steps,
    #         "adamw episilon": 1e-15,
    #     }wandb
    # )
    # If passed along, set the training seed now.
    if args.seed is not None:
        set_seed(args.seed)

    # Handle the repository creation
    if accelerator.is_main_process:
        if args.output_dir is not None:
            os.makedirs(args.output_dir, exist_ok=True)

        if args.push_to_hub:
            create_repo(
                repo_id=args.hub_model_id or Path(args.output_dir).name,
                exist_ok=True,
                token=args.hub_token,
                private=True,
            ).repo_id

    # 1. Create the noise scheduler and the desired noise schedule.
    # scheduler_config = dict(
    #     type="iddpm",
    #     timestep_respacing=""
    # )
    scheduler_config = dict(
        type="rflow",
        use_timestep_transform=True,
        sample_method="logit-normal",
        num_sampling_steps=1000,
        cfg_scale=7.0
    )
    noise_scheduler = build_module(scheduler_config, SCHEDULERS)
    # scheduler_config = dict(
    #     type="rflow",
    #     use_timestep_transform=True,
    #     sample_method="logit-normal",
    # )


    # The scheduler calculates the alpha and sigma schedule for us
    # alpha_schedule = torch.sqrt(noise_scheduler.alphas_cumprod)
    # sigma_schedule = torch.sqrt(1 - noise_scheduler.alphas_cumprod)
    
    # 2. Load tokenizers from SD-XL checkpoint.
   
    # 3. Load text encoders from SD-1.5 checkpoint.
    # import correct text encoder classes
    text_encoder_config = dict(
        type="t5",
        from_pretrained="/g0022010zlc/zyz/Open-Sora-v1.2/weights/T5",
        cache_dir=None,  # "/mnt/hdd/cached_models",
        model_max_length=300,
    )
    text_encoder = build_module(text_encoder_config, MODELS)
    tokenizer = text_encoder.t5.tokenizer
    
    
    # vae_cfg = dict(
    #     type="VideoAutoencoderKL",
    #     from_pretrained="/mount/ccai_nas2/yunzhu/OpenSora-VAE-v1.2/",
    #     cache_dir=None,  # "/mnt/hdd/cached_models",
    #     micro_batch_size=4,
    # )
    vae_cfg = dict(
        type="OpenSoraVAE_V1_2",
        from_pretrained="/g0022010zlc/zyz/Open-Sora-v1.2/weights/OpenSora-VAE-v1.2",
        micro_frame_size=17,
        micro_batch_size=4,
    )
    vae = build_module(vae_cfg, MODELS)

    # 5. Load teacher U-Net from SD-XL checkpoint
    
    dataset_cfg = dict(
        type="VideoTextDataset",
        data_path="/g0022010zlc/zyz/opensora_distill/data/webvid_and_opensoraplan.csv",
        num_frames=17,
        frame_interval=3,
        image_size = (args.resolution, args.resolution),
        transform_name="resize_crop",
    )
    dataset = build_module(dataset_cfg, DATASETS)
    train_dataloader = DataLoader(
        dataset,
        batch_size=args.train_batch_size,
        drop_last=True,
        pin_memory=True,
        num_workers=args.train_batch_size,
    )
    
    student_cfg = dict(
        type="STDiT3-XL/2",
        from_pretrained="/g0022010zlc/zyz/Open-Sora-v1.2/weights/OpenSora-STDiT-v3",
        qk_norm=True,
        enable_flash_attn=False,
        enable_layernorm_kernel=True,
        freeze_y_embedder=True,
    )
    teacher_cfg = dict(
        type="STDiT3-XL/2",
        from_pretrained="/g0022010zlc/zyz/Open-Sora-v1.2/weights/OpenSora-STDiT-v3",
        qk_norm=True,
        enable_flash_attn=False,
        enable_layernorm_kernel=True,
        freeze_y_embedder=True,
    )
    target_cfg = dict(
        type="STDiT3-XL/2",
        from_pretrained="/g0022010zlc/zyz/Open-Sora-v1.2/weights/OpenSora-STDiT-v3",
        qk_norm=True,
        enable_flash_attn=False,
        enable_layernorm_kernel=True,
        freeze_y_embedder=True,
    )
    input_size = (dataset.num_frames, *dataset.image_size)
    latent_size = vae.get_latent_size(input_size)
    teacher_dit = build_module(
        teacher_cfg,
        MODELS,
        input_size=latent_size,
        in_channels=vae.out_channels,
        caption_channels=text_encoder.output_dim,
        model_max_length=text_encoder.model_max_length
    )
    target_dit = build_module(
        target_cfg,
        MODELS,
        input_size=latent_size,
        in_channels=vae.out_channels,
        caption_channels=text_encoder.output_dim,
        model_max_length=text_encoder.model_max_length
    )
    
    # 6. Freeze teacher vae, text_encoder, and teacher_unet
    vae.requires_grad_(False)
    text_encoder.t5.model.requires_grad_(False)
    teacher_dit.requires_grad_(False)
    target_dit.train()
    target_dit.requires_grad_(False)
    
    # 7. Create online (`unet`) student U-Nets.
    dit = build_module(
        student_cfg,
        MODELS,
        input_size=latent_size,
        in_channels=vae.out_channels,
        caption_channels=text_encoder.output_dim,
        model_max_length=text_encoder.model_max_length
    )
    dit.train()
    
    if args.freeze_embed:
        dit.x_embedder.requires_grad_(False)
        dit.t_embedder.requires_grad_(False)
        dit.fps_embedder.requires_grad_(False)
        dit.y_embedder.requires_grad_(False)
        dit.t_block.requires_grad_(False)
    # for name, param  in dit.named_parameters():
    #     if param.requires_grad == True:
    #         print(name)

    # Check that all trainable models are in full precision
    low_precision_error_string = (
        " Please make sure to always have all model weights in full float32 precision when starting training - even if"
        " doing mixed precision training, copy of the weights should still be float32."
    )

    if accelerator.unwrap_model(dit).dtype != torch.float32:
        raise ValueError(
            f"Controlnet loaded as datatype {accelerator.unwrap_model(dit).dtype}. {low_precision_error_string}"
        )

    # 8. Add LoRA to the student U-Net, only the LoRA projection matrix will be updated by the optimizer.
    # for name, param in dit.named_parameters():
    #     print(name)
    

    # 9. Handle mixed precision and device placement
    # For mixed precision training we cast all non-trainable weigths to half-precision
    # as these weights are only used for inference, keeping weights in full precision is not required.
    weight_dtype = torch.float32
    if accelerator.mixed_precision == "fp16":
        weight_dtype = torch.float16
    elif accelerator.mixed_precision == "bf16":
        weight_dtype = torch.bfloat16

    # Move unet, vae and text_encoder to device and cast to weight_dtype
    # The VAE is in float32 to avoid NaN losses.
    vae.to(accelerator.device, dtype=torch.float32)
    # if args.pretrained_vae_model_name_or_path is not None:
    #     vae.to(dtype=weight_dtype)
    text_encoder.t5.model.to(accelerator.device, dtype=torch.float32)

    # Move teacher_unet to device, optionally cast to weight_dtype
    teacher_dit.to(accelerator.device, torch.float32)
    target_dit.to(accelerator.device, torch.float32)
    dit.to(accelerator.device, torch.float32)
    # if args.cast_teacher_unet:
    #     teacher_dit.to(dtype=weight_dtype)

    # Also move the alpha and sigma noise schedules to accelerator.device.
    
  

    # 10. Handle saving and loading of checkpoints
    # `accelerate` 0.16.0 will have better support for customized saving
    if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
        # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
        def save_model_hook(models, weights, output_dir):
            if accelerator.is_main_process:
                #dit_ = accelerator.unwrap_model(dit)
                #lora_state_dict = get_peft_model_state_dict(dit_, adapter_name="default")
                #StableDiffusionPipeline.save_lora_weights(os.path.join(output_dir, "dit_lora"), lora_state_dict)
                # save weights in peft format to be able to load them back
                target_dit.save_pretrained(os.path.join(output_dir, "model"))
                for _, model in enumerate(models):
                    # make sure to pop weight so that corresponding model is not saved again
                    weights.pop()

        def load_model_hook(models, input_dir):
            # load the LoRA into the model
            from opensora.utils.ckpt_utils import load_checkpoint
            print("---"*5, input_dir)
            load_checkpoint(target_dit, input_dir)
            load_checkpoint(dit, input_dir) 
            for _ in range(len(models)):
                # pop models so that they are not loaded again
                models.pop()

        accelerator.register_save_state_pre_hook(save_model_hook)
        accelerator.register_load_state_pre_hook(load_model_hook)

    # 11. Enable optimizations
    if args.enable_xformers_memory_efficient_attention:
        if is_xformers_available():
            import xformers

            xformers_version = version.parse(xformers.__version__)
            if xformers_version == version.parse("0.0.16"):
                logger.warn(
                    "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
                )
            dit.enable_xformers_memory_efficient_attention()
            teacher_dit.enable_xformers_memory_efficient_attention()
            # target_unet.enable_xformers_memory_efficient_attention()
        else:
            raise ValueError("xformers is not available. Make sure it is installed correctly")

    # Enable TF32 for faster training on Ampere GPUs,
    # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
    if args.allow_tf32:
        torch.backends.cuda.matmul.allow_tf32 = True

    if args.gradient_checkpointing:
        dit.enable_gradient_checkpointing()

    # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
    if args.use_8bit_adam:
        try:
            import bitsandbytes as bnb
        except ImportError:
            raise ImportError(
                "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
            )

        optimizer_class = bnb.optim.AdamW8bit
    else:
        optimizer_class = torch.optim.AdamW

    # 12. Optimizer creation
    optimizer = optimizer_class(
        dit.parameters(),
        lr=args.learning_rate,
        betas=(args.adam_beta1, args.adam_beta2),
        weight_decay=args.adam_weight_decay,
        eps=args.adam_epsilon,
    )

    # Here, we compute not just the text embeddings but also the additional embeddings
    # needed for the SD XL UNet to operate.
    
    

    # Scheduler and math around the number of training steps.
    overrode_max_train_steps = False
    # one_batch_size = train_dataloader.batch_size // num_of_gpu
    num_update_steps_per_epoch = math.ceil((len(dataset) / train_dataloader.batch_size) / args.gradient_accumulation_steps)
    if args.max_train_steps is None:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
        overrode_max_train_steps = True

    lr_scheduler = get_scheduler(
        args.lr_scheduler,
        optimizer=optimizer,
        num_warmup_steps=args.lr_warmup_steps,
        num_training_steps=args.max_train_steps,
    )
    text_encoder.y_embedder = dit.y_embedder

    # Prepare everything with our `accelerator`.
    dit, optimizer, lr_scheduler, train_dataloader = accelerator.prepare(dit, optimizer, lr_scheduler, train_dataloader)

    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
    #num_update_steps_per_epoch = math.ceil((len(dataset) / train_dataloader.batch_size) / args.gradient_accumulation_steps)
    if overrode_max_train_steps:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
    # Afterwards we recalculate our number of training epochs
    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)

    # We need to initialize the trackers we use, and also store our configuration.
    # The trackers initializes automatically on the main process.
    if accelerator.is_main_process:
        tracker_config = dict(vars(args))
        accelerator.init_trackers(args.tracker_project_name, config=tracker_config)

   
   
    #print("uncond prompt embeds:", uncond_prompt_embeds.shape, "num gpu:", num_of_gpu)

    # Train!
    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps

    logger.info("***** Running training *****")
    logger.info(f"  Num batches each epoch = {len(dataset) / args.train_batch_size}")
    logger.info(f"  Num Epochs = {args.num_train_epochs}")
    logger.info(f"  accelerate num processes = {accelerator.num_processes}")
    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
    logger.info(f"  Total optimization steps = {args.max_train_steps}")
    
    # model_args["fps"] = torch.tensor([16], device=accelerator.device).repeat(one_batch_size)
    # solver = EulerSolver(
    #     sample_timesteps=args.num_ddim_timesteps,
    #     model_args=model_args
    # )
    # solver = solver.to(accelerator.device)
    

    stage_one_epoch = 2500
    global_step = 0
    first_epoch = 0

    # Potentially load in the weights and states from a previous save
    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint != "latest":
            path = os.path.basename(args.resume_from_checkpoint)
        else:
            # Get the most recent checkpoint
            dirs = os.listdir(args.output_dir)
            dirs = [d for d in dirs if d.startswith("checkpoint")]
            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
            path = dirs[-1] if len(dirs) > 0 else None

        if path is None:
            accelerator.print(
                f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
            )
            args.resume_from_checkpoint = None
            initial_global_step = 0
        else:
            accelerator.print(f"Resuming from checkpoint {path}")
            accelerator.load_state(os.path.join(args.output_dir, path))
            global_step = int(path.split("-")[1])

            initial_global_step = global_step
            first_epoch = global_step // num_update_steps_per_epoch
    else:
        initial_global_step = 0

    progress_bar = tqdm(
        range(0, args.max_train_steps),
        initial=initial_global_step,
        desc="Steps",
        # Only show the progress bar once on each machine.
        disable=not accelerator.is_local_main_process,
    )
    rank = accelerator.process_index
    torch.manual_seed(rank*1024)
    distribution = LogisticNormal(torch.tensor([0.0]), torch.tensor([1.0]))
   
    for epoch in range(first_epoch, args.num_train_epochs):
        for step, batch in enumerate(train_dataloader):
            with accelerator.accumulate(dit):
                #video = batch['video'].to(accelerator.device, weight_dtype)
                #text = batch['text']
                #model_args["fps"] = batch.pop("fps").to(accelerator.device, weight_dtype)
                #video = video.to(accelerator.device, non_blocking=True)
                ## encoded_text = compute_embeddings_fn(text)
                ##model_args = text_encoder.encode(text)

                ## pixel_values = video.to(dtype=weight_dtype)
                video = batch.pop('video').to(accelerator.device, weight_dtype)
                text = batch.pop('text')
                flow = batch.pop("flow").cpu().numpy()
                aes = batch.pop("aes").cpu().numpy()
                
                texts = []
                for index, t in enumerate(text):
                    texts.append(f"{t}. aesthetic score : {aes[index]:.1f}. motion score: {flow[index]:.1f}")
                batch_prompts = [text_preprocessing(t) for t in texts]
                batch_prompts_embed = text_encoder.encode(batch_prompts)["y"] 
                
                video = video.to(accelerator.device, non_blocking=True)
                if vae.dtype != weight_dtype:
                    vae.to(dtype=weight_dtype)

                # encode pixel values with batch size of at most 32
          
                
                latents = vae.encode(video)
                bsz = latents.shape[0]
                uncond_prompt_embeds = text_encoder.encode([""] * (bsz))["y"]
                model_args = dict()
                model_args["height"] = torch.tensor([dataset.image_size[0]], device=accelerator.device).repeat(bsz)
                model_args["width"]  = torch.tensor([dataset.image_size[1]], device=accelerator.device).repeat(bsz)
                model_args["num_frames"] = torch.tensor([dataset.num_frames], device=accelerator.device).repeat(bsz)
                model_args["ar"]  = torch.tensor([dataset.image_size[0] / dataset.image_size[1]], device=accelerator.device).repeat(bsz)
                model_args["fps"] = batch.pop("fps").to(accelerator.device, weight_dtype)

                #latents = latents * vae.config.scaling_factor
                #latents = latents * vae.module.config.scaling_factor
                latents = latents.to(weight_dtype)

                t = distribution.sample((latents.shape[0],))[:, 0].to(latents.device) * 1000
                # print("sample t:", t)
                # print("texts:", texts)
                # print(accelerator.device, weight_dtype)
                stage = min(global_step // stage_one_epoch + 1, 7)
                delta_t = stage * 30
                while torch.any(t < delta_t):
                    t = distribution.sample((latents.shape[0],))[:, 0].to(latents.device) * 1000 
                s = t - delta_t
                t_transformed = torch.tensor([timestep_transform(timesteps, model_args, num_timesteps=1000) for timesteps in t]).to(latents.device)
                s_transformed =  torch.tensor([timestep_transform(timesteps, model_args, num_timesteps=1000) for timesteps in s]).to(latents.device)
                delta_t_transformed = t_transformed - s_transformed
                noise =torch.randn_like(latents)
                noisy_model_input = noise_scheduler.scheduler.add_noise(latents, noise, t).to()
        
               
               
                # w = (0) * torch.rand((bsz,)) + 4
                # w = w.reshape(bsz, 1, 1, 1, 1)
                # w = w.to(device=latents.device, dtype=latents.dtype)
                w = (args.w_max - args.w_min) * torch.rand((bsz,)) + args.w_min
                w = w.reshape(bsz, 1, 1, 1, 1)
                w = w.to(device=latents.device, dtype=latents.dtype)
                # w = (0) * torch.rand((bsz,)) + 4
                w = w.reshape(bsz, 1, 1, 1, 1)
                w = w.to(device=latents.device, dtype=latents.dtype)


                model_args["y"] = batch_prompts_embed
                v_pred_student = dit(noisy_model_input, t_transformed, **model_args)
                #print(v_pred_student, t_transformed, noisy_model_input)
                B, C = v_pred_student.shape[:2]
                v_pred_student, _ = torch.split(v_pred_student, C // 2, dim=1)
                x_i = noisy_model_input
                last_transformed_t = t_transformed
                with torch.no_grad():
                    for i in range(1, stage + 1):
                        t_i = t - i * 30
                        t_i = torch.clamp(t_i, min=0)
                        t_i_transformed = torch.tensor([timestep_transform(timesteps, model_args, num_timesteps=1000) for timesteps in t_i]).to(latents.device)
                        model_args["y"] = batch_prompts_embed
                        v_pred_teacher_cond = teacher_dit(x_i, t_i_transformed, **model_args)
                        model_args["y"] = uncond_prompt_embeds
                        v_pred_teacher_uncond = teacher_dit(x_i, t_i_transformed, **model_args) 
                        v_pred_teacher = v_pred_teacher_uncond + w * (v_pred_teacher_cond - v_pred_teacher_uncond) 
                        v_pred_teacher, _ = torch.split(v_pred_teacher, C // 2, dim=1)
                        x_i = x_i + v_pred_teacher * (last_transformed_t - t_i_transformed).view(-1, 1, 1, 1, 1) / 1000 
                        last_transformed_t = t_i_transformed
                teacher_accumulate = (x_i - noisy_model_input)*1000 / delta_t_transformed.view(-1, 1, 1, 1, 1)
                student_accumulate = v_pred_student


                
                # loss = train_scheduler.training_losses(dit, pred_x_0, model_kwargs=model_args, noise=noise)["loss"]
                # print(student_accumulate.float().mean()  - teacher_accumulate.float().mean())
                #print(student_accumulate, teacher_accumulate)
                #loss =  torch.mean(torch.sqrt((student_accumulate.float() - teacher_accumulate.float()) - args.huber_c**2) - args.huber_c)
                loss =  torch.mean(torch.sqrt((student_accumulate.float()  - teacher_accumulate.float()) ** 2 + args.huber_c**2) - args.huber_c)
                accelerator.backward(loss)
                if accelerator.sync_gradients:
                    accelerator.clip_grad_norm_(dit.parameters(), args.max_grad_norm)
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad(set_to_none=True)

            # Checks if the accelerator has performed an optimization step behind the scenes
            if accelerator.sync_gradients:
                update_ema(target_dit.parameters(), dit.parameters(), 0.95)
                progress_bar.update(1)
                global_step += 1

                if accelerator.is_main_process:
                    if global_step % args.checkpointing_steps == 0:
                        # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
                        if args.checkpoints_total_limit is not None:
                            checkpoints = os.listdir(args.output_dir)
                            checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
                            checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))

                            # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
                            if len(checkpoints) >= args.checkpoints_total_limit:
                                num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
                                removing_checkpoints = checkpoints[0:num_to_remove]

                                logger.info(
                                    f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
                                )
                                logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")

                                for removing_checkpoint in removing_checkpoints:
                                    removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
                                    shutil.rmtree(removing_checkpoint)

                        save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
                        accelerator.save_state(save_path)
                        logger.info(f"Saved state to {save_path}")

                    if global_step % args.validation_steps == 0:
                        # log_validation(vae, target_dit, args, accelerator, weight_dtype, global_step, text_encoder,  model_args, mode="offline")
                        log_validation(vae, target_dit, args, accelerator, torch.float32, global_step, text_encoder, mode="online")
            del model_args["y"]
            logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0], "stage" : stage, "delta_t": [delta_t_transformed[i].detach().item() for i in range(bsz)]}
            progress_bar.set_postfix(**logs)
            accelerator.log(logs, step=global_step)
            # wandb.log(logs)

            if global_step >= args.max_train_steps:
                break


    # Create the pipeline using using the trained modules and save it.
    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
        dit = accelerator.unwrap_model(dit)
        dit.save_pretrained(args.output_dir)
    accelerator.end_training()


if __name__ == "__main__":
    args = parse_args()
    main(args)


